BACKGROUND: Data collected in controlled settings typically results in high-quality datasets. However, in real-world applications, the quality of data collection is often compromised. It is well established that the quality of a dataset significantly...
BACKGROUND: As data-driven medical research advances, vast amounts of medical data are being collected, giving researchers access to important information. However, issues such as heterogeneity, complexity, and incompleteness of datasets limit their ...
BACKGROUND: Integrated health data are foundational for secondary use, research, and policymaking. However, data quality issues-such as missing values and inconsistencies-are common due to the heterogeneity of health data sources. Existing frameworks...
AIM: This study explores the semantic similarities between qualitative research transcripts produced by artificial intelligence (AI) and those transcribed manually, with a particular focus on challenges encountered when working with multicultural par...
The rapid uptake of supervised machine learning (ML) in clinical prediction modelling, particularly for binary outcomes based on tabular data, has sparked debate about its comparative advantage over traditional statistical logistic regression. Althou...
As the primary link in cybersecurity, the intrusion detection system (IDS) is of indispensable importance. Many studies have proposed sophisticated artificial intelligence (AI) models to detect intrusion behavior from a large amount of data, yet they...
As crowdsourcing emerges as an efficient and cost-effective method for obtaining labels for machine learning datasets, it is important to assess the quality of crowd-provided data to improve analysis performance and reduce biases in subsequent machin...
BACKGROUND: Evidence-based medicine combines scientific research, clinical expertise, and patient preferences to enhance the patient outcomes and improve health care quality. Clinical data are crucial in aligning medical decisions with evidence-based...
Eye-tracking is widely used to measure human attention in research, commercial, and clinical applications. With the rapid advancements in artificial intelligence and mobile computing, deep learning algorithms for computer vision-based eye tracking ha...
OBJECTIVE: Medical concept normalization (MCN) aims to map informal medical terms to formal medical concepts, a critical task in building machine learning systems for medical applications. However, most existing studies on MCN primarily focus on mode...
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